pattern recognition

artificial intelligence, data processing A branch of

artificial intelligence concerned with the classification or

description of observations.

Pattern recognition aims to classify data (patterns) based

on either a priori knowledge or on statistical information

extracted from the patterns. The patterns to be classified

are usually groups of measurements or observations, defining

points in an appropriate multidimensional space.

A complete pattern recognition system consists of a sensor

that gathers the observations to be classified or described; a

feature extraction mechanism that computes numeric or

symbolic information from the observations; and a

classification or description scheme that does the actual job

of classifying or describing observations, relying on the

extracted features.

The classification or description scheme is usually based on

the availability of a set of patterns that have already been

classified or described. This set of patterns is termed the

training set and the resulting learning strategy is

characterised as supervised. Learning can also be

unsupervised, in the sense that the system is not given an a

priori labelling of patterns, instead it establishes the

classes itself based on the statistical regularities of the

patterns.

The classification or description scheme usually uses one of

the following approaches: statistical (or decisiontheoretic), syntactic (or structural), or neural.

Statistical pattern recognition is based on statistical

characterisations of patterns, assuming that the patterns are

generated by a probabilistic system. Structural pattern

recognition is based on the structural interrelationships of

features. Neural pattern recognition employs the neural

computing paradigm that has emerged with neural networks.

(1995-09-22)